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Record W2796248606 · doi:10.1515/hsz-2018-0112

The miRNA-kallikrein interaction: a mosaic of epigenetic regulation in cancer

2018· review· en· W2796248606 on OpenAlexaff
Ashley Di Meo, Cong Wang, Yufeng Cheng, Eleftherios P. Diamandis, George M. Yousef

Bibliographic record

VenueBiological Chemistry · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMount Sinai HospitalUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsEpigeneticsmicroRNAMosaicKallikreinComputational biologyBiologyGeneticsChemistryBiochemistryGeneGeography

Abstract

fetched live from OpenAlex

The kallikrein-related peptidases (KLKs) constitute a family of 15 highly conserved serine proteases with trypsin- and chymotrypsin-like activities. Dysregulated expression and/or aberrant activation of KLKs has been linked to various pathophysiological processes, including cancer. Many KLKs have been identified as potential cancer biomarkers. microRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene expression by pairing to the 3' untranslated region (UTR) of complimentary mRNA targets. miRNAs are dysregulated in many cancers, including prostate, kidney and ovarian cancers. Several studies have shown that miRNAs are involved in the post-transcriptional regulation of KLKs. However, recent evidence suggests that miRNAs can also act as downstream effectors of KLKs. In this review, we provide an update on the epigenetic regulation of KLKs by miRNAs. We also present recent experimental evidence that supports the regulatory role of KLKs on miRNA networks. The potential diagnostic and therapeutic applications of miRNA-kallikrein interactions are also discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.348
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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